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  <h1>Source code for torch_tensorrt.fx.fx2trt</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">logging</span>
<span class="kn">import</span> <span class="nn">warnings</span>
<span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Any</span><span class="p">,</span> <span class="n">Callable</span><span class="p">,</span> <span class="n">Dict</span><span class="p">,</span> <span class="n">List</span><span class="p">,</span> <span class="n">NamedTuple</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Sequence</span>

<span class="kn">import</span> <span class="nn">numpy</span>

<span class="c1"># @manual=//deeplearning/trt/python:py_tensorrt</span>
<span class="kn">import</span> <span class="nn">tensorrt</span> <span class="k">as</span> <span class="nn">trt</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torch.fx</span>
<span class="kn">from</span> <span class="nn">torch.fx.node</span> <span class="kn">import</span> <span class="n">_get_qualified_name</span>
<span class="kn">from</span> <span class="nn">torch.fx.passes.shape_prop</span> <span class="kn">import</span> <span class="n">TensorMetadata</span>

<span class="kn">from</span> <span class="nn">.converter_registry</span> <span class="kn">import</span> <span class="n">CONVERTERS</span>
<span class="kn">from</span> <span class="nn">.input_tensor_spec</span> <span class="kn">import</span> <span class="n">InputTensorSpec</span>
<span class="kn">from</span> <span class="nn">.observer</span> <span class="kn">import</span> <span class="n">Observer</span>
<span class="kn">from</span> <span class="nn">.utils</span> <span class="kn">import</span> <span class="n">get_dynamic_dims</span><span class="p">,</span> <span class="n">LowerPrecision</span><span class="p">,</span> <span class="n">torch_dtype_to_trt</span>

<span class="n">_LOGGER</span><span class="p">:</span> <span class="n">logging</span><span class="o">.</span><span class="n">Logger</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>

<span class="n">TRT_INTERPRETER_CALL_PRE_OBSERVER</span><span class="p">:</span> <span class="n">Observer</span><span class="p">[</span>
    <span class="n">Callable</span><span class="p">[[</span><span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">],</span> <span class="kc">None</span><span class="p">]</span>
<span class="p">]</span> <span class="o">=</span> <span class="n">Observer</span><span class="p">(</span><span class="s2">&quot;TRT_INTERPRETER_CALL_PRE_OBSERVER&quot;</span><span class="p">)</span>


<div class="viewcode-block" id="TRTInterpreterResult"><a class="viewcode-back" href="../../../py_api/fx.html#torch_tensorrt.fx.TRTInterpreterResult">[docs]</a><span class="k">class</span> <span class="nc">TRTInterpreterResult</span><span class="p">(</span><span class="n">NamedTuple</span><span class="p">):</span>
    <span class="n">engine</span><span class="p">:</span> <span class="n">Any</span>
    <span class="n">input_names</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span>
    <span class="n">output_names</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span>
    <span class="n">serialized_cache</span><span class="p">:</span> <span class="nb">bytearray</span></div>


<div class="viewcode-block" id="TRTInterpreter"><a class="viewcode-back" href="../../../py_api/fx.html#torch_tensorrt.fx.TRTInterpreter">[docs]</a><span class="k">class</span> <span class="nc">TRTInterpreter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">Interpreter</span><span class="p">):</span>
    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
        <span class="bp">self</span><span class="p">,</span>
        <span class="n">module</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">,</span>
        <span class="n">input_specs</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">InputTensorSpec</span><span class="p">],</span>
        <span class="n">explicit_batch_dimension</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
        <span class="n">explicit_precision</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
        <span class="n">logger_level</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
    <span class="p">):</span>
        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">module</span><span class="p">)</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">logger</span> <span class="o">=</span> <span class="n">trt</span><span class="o">.</span><span class="n">Logger</span><span class="p">(</span><span class="n">logger_level</span> <span class="ow">or</span> <span class="n">trt</span><span class="o">.</span><span class="n">Logger</span><span class="o">.</span><span class="n">WARNING</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">builder</span> <span class="o">=</span> <span class="n">trt</span><span class="o">.</span><span class="n">Builder</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">logger</span><span class="p">)</span>

        <span class="n">flag</span> <span class="o">=</span> <span class="mi">0</span>
        <span class="k">if</span> <span class="n">explicit_batch_dimension</span><span class="p">:</span>
            <span class="n">EXPLICIT_BATCH</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">&lt;&lt;</span> <span class="p">(</span><span class="nb">int</span><span class="p">)(</span>
                <span class="n">trt</span><span class="o">.</span><span class="n">NetworkDefinitionCreationFlag</span><span class="o">.</span><span class="n">EXPLICIT_BATCH</span>
            <span class="p">)</span>
            <span class="n">flag</span> <span class="o">|=</span> <span class="n">EXPLICIT_BATCH</span>

        <span class="k">if</span> <span class="n">explicit_precision</span><span class="p">:</span>
            <span class="n">EXPLICIT_PRECISION</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">&lt;&lt;</span> <span class="p">(</span><span class="nb">int</span><span class="p">)(</span>
                <span class="n">trt</span><span class="o">.</span><span class="n">NetworkDefinitionCreationFlag</span><span class="o">.</span><span class="n">EXPLICIT_PRECISION</span>
            <span class="p">)</span>
            <span class="n">flag</span> <span class="o">|=</span> <span class="n">EXPLICIT_PRECISION</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">network</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">builder</span><span class="o">.</span><span class="n">create_network</span><span class="p">(</span><span class="n">flag</span><span class="p">)</span>

        <span class="n">missing_ops</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">validate_conversion</span><span class="p">()</span>
        <span class="k">if</span> <span class="n">missing_ops</span><span class="p">:</span>
            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
                <span class="s2">&quot;Interpretation will fail due to missing operations </span><span class="se">\n</span><span class="s2">&quot;</span>
                <span class="o">+</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">&quot;</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">missing_ops</span><span class="p">)</span>
            <span class="p">)</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">optimization_profiles</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">input_specs</span> <span class="o">=</span> <span class="n">input_specs</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">input_specs_iter</span> <span class="o">=</span> <span class="mi">0</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">validate_input_specs</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_cur_node_name</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_input_names</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_output_names</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_itensor_to_tensor_meta</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span>
            <span class="n">trt</span><span class="o">.</span><span class="n">tensorrt</span><span class="o">.</span><span class="n">ITensor</span><span class="p">,</span> <span class="n">TensorMetadata</span>
        <span class="p">]</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>

    <span class="k">def</span> <span class="nf">validate_input_specs</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="k">for</span> <span class="n">shape</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">shape_ranges</span><span class="p">,</span> <span class="n">has_batch_dim</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">input_specs</span><span class="p">:</span>
            <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">network</span><span class="o">.</span><span class="n">has_implicit_batch_dimension</span><span class="p">:</span>
                <span class="k">assert</span> <span class="p">(</span>
                    <span class="n">has_batch_dim</span>
                <span class="p">),</span> <span class="s2">&quot;It&#39;s required to specify batch dimension when it&#39;s explicit in TensorRT network.&quot;</span>

            <span class="n">dynamic_dims</span> <span class="o">=</span> <span class="n">get_dynamic_dims</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span>
            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">dynamic_dims</span><span class="p">):</span>
                <span class="k">assert</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">network</span><span class="o">.</span><span class="n">has_implicit_batch_dimension</span><span class="p">,</span> <span class="p">(</span>
                    <span class="s2">&quot;Can&#39;t have dynamic dim when &quot;</span>
                    <span class="sa">f</span><span class="s2">&quot;batch dim is implicit, got </span><span class="si">{</span><span class="n">shape</span><span class="si">}</span><span class="s2">.&quot;</span>
                <span class="p">)</span>
                <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span>
                    <span class="n">shape_ranges</span>
                <span class="p">),</span> <span class="s2">&quot;shape_ranges must be provided when shape has dynamic dim.&quot;</span>

                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">optimization_profiles</span><span class="p">:</span>
                    <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">shape_ranges</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">optimization_profiles</span><span class="p">),</span> <span class="p">(</span>
                        <span class="s2">&quot;Number of optimization &quot;</span>
                        <span class="sa">f</span><span class="s2">&quot;profiles </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">optimization_profiles</span><span class="p">)</span><span class="si">}</span><span class="s2"> doesn&#39;t match with the number of shape_range&quot;</span>
                        <span class="sa">f</span><span class="s2">&quot; </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">shape_ranges</span><span class="p">)</span><span class="si">}</span><span class="s2"> provided.&quot;</span>
                    <span class="p">)</span>
                <span class="k">else</span><span class="p">:</span>
                    <span class="bp">self</span><span class="o">.</span><span class="n">optimization_profiles</span> <span class="o">=</span> <span class="p">[</span>
                        <span class="bp">self</span><span class="o">.</span><span class="n">builder</span><span class="o">.</span><span class="n">create_optimization_profile</span><span class="p">()</span>
                        <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">shape_ranges</span><span class="p">))</span>
                    <span class="p">]</span>

                <span class="k">for</span> <span class="n">shape_range</span> <span class="ow">in</span> <span class="n">shape_ranges</span><span class="p">:</span>
                    <span class="k">assert</span> <span class="p">(</span>
                        <span class="nb">len</span><span class="p">(</span><span class="n">shape_range</span><span class="p">)</span> <span class="o">==</span> <span class="mi">3</span>
                    <span class="p">),</span> <span class="sa">f</span><span class="s2">&quot;Expect three elements in shape_range, got </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">shape_range</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span>
                    <span class="k">assert</span> <span class="nb">all</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">s</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">shape_range</span><span class="p">),</span> <span class="p">(</span>
                        <span class="s2">&quot;Expect elements in shape_range&quot;</span>
                        <span class="sa">f</span><span class="s2">&quot; </span><span class="si">{</span><span class="n">shape_range</span><span class="si">}</span><span class="s2"> have the same number of dimension as the provided shape </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span>
                    <span class="p">)</span>

                    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">shape</span><span class="p">)):</span>
                        <span class="k">if</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">dynamic_dims</span><span class="p">:</span>
                            <span class="k">assert</span> <span class="nb">all</span><span class="p">(</span>
                                <span class="n">shape_range</span><span class="p">[</span><span class="n">j</span><span class="p">][</span><span class="n">i</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">shape_range</span><span class="p">[</span><span class="n">j</span> <span class="o">+</span> <span class="mi">1</span><span class="p">][</span><span class="n">i</span><span class="p">]</span>
                                <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
                            <span class="p">),</span> <span class="p">(</span>
                                <span class="s2">&quot;Expect dynamic dim&quot;</span>
                                <span class="sa">f</span><span class="s2">&quot; </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2"> to have incremental value for shapes in shape_range </span><span class="si">{</span><span class="n">shape_range</span><span class="si">}</span><span class="s2">.&quot;</span>
                            <span class="p">)</span>
                        <span class="k">else</span><span class="p">:</span>
                            <span class="k">assert</span> <span class="nb">all</span><span class="p">(</span><span class="n">s</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">==</span> <span class="n">shape</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">shape_range</span><span class="p">),</span> <span class="p">(</span>
                                <span class="sa">f</span><span class="s2">&quot;Expect non dynamic dim </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2"> to be the same&quot;</span>
                                <span class="sa">f</span><span class="s2">&quot; for all shapes in shape_range </span><span class="si">{</span><span class="n">shape_range</span><span class="si">}</span><span class="s2">.&quot;</span>
                            <span class="p">)</span>
            <span class="k">else</span><span class="p">:</span>
                <span class="k">assert</span> <span class="p">(</span>
                    <span class="nb">len</span><span class="p">(</span><span class="n">shape_ranges</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span>
                <span class="p">),</span> <span class="s2">&quot;shape_ranges are provided for input that doesn&#39;t have dynamic dim.&quot;</span>

    <span class="k">def</span> <span class="nf">validate_conversion</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="n">missing_converter</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>

        <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">module</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">nodes</span><span class="p">:</span>
            <span class="k">if</span> <span class="n">node</span><span class="o">.</span><span class="n">op</span> <span class="o">==</span> <span class="s2">&quot;call_function&quot;</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">CONVERTERS</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">node</span><span class="o">.</span><span class="n">target</span><span class="p">):</span>
                <span class="n">missing_converter</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">node</span><span class="o">.</span><span class="n">op</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="n">_get_qualified_name</span><span class="p">(</span><span class="n">node</span><span class="o">.</span><span class="n">target</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
            <span class="k">elif</span> <span class="n">node</span><span class="o">.</span><span class="n">op</span> <span class="o">==</span> <span class="s2">&quot;call_method&quot;</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">CONVERTERS</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">node</span><span class="o">.</span><span class="n">target</span><span class="p">):</span>
                <span class="n">missing_converter</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">node</span><span class="o">.</span><span class="n">op</span><span class="si">}</span><span class="s2"> torch.Tensor.</span><span class="si">{</span><span class="n">node</span><span class="o">.</span><span class="n">target</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
            <span class="k">elif</span> <span class="n">node</span><span class="o">.</span><span class="n">op</span> <span class="o">==</span> <span class="s2">&quot;call_module&quot;</span><span class="p">:</span>
                <span class="n">submod</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fetch_attr</span><span class="p">(</span><span class="n">node</span><span class="o">.</span><span class="n">target</span><span class="p">)</span>
                <span class="n">submod_type</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">submod</span><span class="p">,</span> <span class="s2">&quot;_base_class_origin&quot;</span><span class="p">,</span> <span class="nb">type</span><span class="p">(</span><span class="n">submod</span><span class="p">))</span>
                <span class="k">if</span> <span class="ow">not</span> <span class="n">CONVERTERS</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">submod_type</span><span class="p">):</span>
                    <span class="n">missing_converter</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">node</span><span class="o">.</span><span class="n">op</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="n">torch</span><span class="o">.</span><span class="n">typename</span><span class="p">(</span><span class="n">submod_type</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>

        <span class="k">return</span> <span class="n">missing_converter</span>

    <span class="k">def</span> <span class="nf">run</span><span class="p">(</span>
        <span class="bp">self</span><span class="p">,</span>
        <span class="n">max_batch_size</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span>
        <span class="n">max_workspace_size</span><span class="o">=</span><span class="mi">1</span> <span class="o">&lt;&lt;</span> <span class="mi">25</span><span class="p">,</span>
        <span class="n">lower_precision</span><span class="o">=</span><span class="n">LowerPrecision</span><span class="o">.</span><span class="n">FP16</span><span class="p">,</span>
        <span class="n">sparse_weights</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
        <span class="n">force_fp32_output</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
        <span class="n">strict_type_constraints</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
        <span class="n">algorithm_selector</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
        <span class="n">timing_cache</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
        <span class="n">profiling_verbosity</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
        <span class="n">tactic_sources</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">TRTInterpreterResult</span><span class="p">:</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Build TensorRT engine with some configs.</span>
<span class="sd">        Args:</span>
<span class="sd">            max_batch_size: set accordingly for maximum batch size you will use.</span>
<span class="sd">            max_workspace_size: set to the maximum size we can afford for temporary buffer</span>
<span class="sd">            lower_precision: the precision model layers are running on (TensorRT will choose the best perforamnce precision).</span>
<span class="sd">            sparse_weights: allow the builder to examine weights and use optimized functions when weights have suitable sparsity</span>
<span class="sd">            force_fp32_output: force output to be fp32</span>
<span class="sd">            strict_type_constraints: Usually we should set it to False unless we want to control the precision of certain layer for numeric reasons.</span>
<span class="sd">            algorithm_selector: set up algorithm selection for certain layer</span>
<span class="sd">            timing_cache: enable timing cache for TensorRT</span>
<span class="sd">            profiling_verbosity: TensorRT logging level</span>
<span class="sd">        Return:</span>
<span class="sd">            TRTInterpreterResult</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="n">TRT_INTERPRETER_CALL_PRE_OBSERVER</span><span class="o">.</span><span class="n">observe</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">module</span><span class="p">)</span>

        <span class="c1"># For float outputs, we set their dtype to fp16 only if lower_precision == LowerPrecision.FP16 and</span>
        <span class="c1"># force_fp32_output=False.</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">output_fp16</span> <span class="o">=</span> <span class="p">(</span>
            <span class="ow">not</span> <span class="n">force_fp32_output</span> <span class="ow">and</span> <span class="n">lower_precision</span> <span class="o">==</span> <span class="n">LowerPrecision</span><span class="o">.</span><span class="n">FP16</span>
        <span class="p">)</span>

        <span class="k">if</span> <span class="p">(</span>
            <span class="n">lower_precision</span> <span class="o">==</span> <span class="n">LowerPrecision</span><span class="o">.</span><span class="n">INT8</span>
            <span class="ow">and</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">builder</span><span class="o">.</span><span class="n">platform_has_fast_int8</span>
        <span class="p">):</span>
            <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;Current platform doesn&#39;t support fast native int8!&quot;</span><span class="p">)</span>

        <span class="k">if</span> <span class="p">(</span>
            <span class="n">lower_precision</span> <span class="o">==</span> <span class="n">LowerPrecision</span><span class="o">.</span><span class="n">FP16</span>
            <span class="ow">and</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">builder</span><span class="o">.</span><span class="n">platform_has_fast_fp16</span>
        <span class="p">):</span>
            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;Current platform doesn&#39;t support fast native fp16!&quot;</span><span class="p">)</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">input_specs_iter</span> <span class="o">=</span> <span class="mi">0</span>
        <span class="n">run_module_start_time</span> <span class="o">=</span> <span class="n">datetime</span><span class="o">.</span><span class="n">now</span><span class="p">()</span>
        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">run</span><span class="p">()</span>
        <span class="n">_LOGGER</span><span class="o">.</span><span class="n">info</span><span class="p">(</span>
            <span class="sa">f</span><span class="s2">&quot;Run Module elapsed time: </span><span class="si">{</span><span class="n">datetime</span><span class="o">.</span><span class="n">now</span><span class="p">()</span> <span class="o">-</span> <span class="n">run_module_start_time</span><span class="si">}</span><span class="s2">&quot;</span>
        <span class="p">)</span>
        <span class="n">build_engine_start_time</span> <span class="o">=</span> <span class="n">datetime</span><span class="o">.</span><span class="n">now</span><span class="p">()</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">builder</span><span class="o">.</span><span class="n">max_batch_size</span> <span class="o">=</span> <span class="n">max_batch_size</span>
        <span class="n">builder_config</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">builder</span><span class="o">.</span><span class="n">create_builder_config</span><span class="p">()</span>
        <span class="n">builder_config</span><span class="o">.</span><span class="n">max_workspace_size</span> <span class="o">=</span> <span class="n">max_workspace_size</span>

        <span class="n">cache</span> <span class="o">=</span> <span class="kc">None</span>
        <span class="k">if</span> <span class="n">timing_cache</span><span class="p">:</span>
            <span class="n">cache_file</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">timing_cache</span><span class="p">)</span>
            <span class="n">cache</span> <span class="o">=</span> <span class="n">builder_config</span><span class="o">.</span><span class="n">create_timing_cache</span><span class="p">(</span><span class="n">cache_file</span><span class="o">.</span><span class="n">tobytes</span><span class="p">())</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">cache</span> <span class="o">=</span> <span class="n">builder_config</span><span class="o">.</span><span class="n">create_timing_cache</span><span class="p">(</span><span class="sa">b</span><span class="s2">&quot;&quot;</span><span class="p">)</span>
        <span class="n">builder_config</span><span class="o">.</span><span class="n">set_timing_cache</span><span class="p">(</span><span class="n">cache</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>

        <span class="k">if</span> <span class="n">trt</span><span class="o">.</span><span class="n">__version__</span> <span class="o">&gt;=</span> <span class="s2">&quot;8.2&quot;</span><span class="p">:</span>
            <span class="n">builder_config</span><span class="o">.</span><span class="n">profiling_verbosity</span> <span class="o">=</span> <span class="p">(</span>
                <span class="n">profiling_verbosity</span>
                <span class="k">if</span> <span class="n">profiling_verbosity</span>
                <span class="k">else</span> <span class="n">trt</span><span class="o">.</span><span class="n">ProfilingVerbosity</span><span class="o">.</span><span class="n">LAYER_NAMES_ONLY</span>
            <span class="p">)</span>
        <span class="k">if</span> <span class="n">lower_precision</span> <span class="o">==</span> <span class="n">LowerPrecision</span><span class="o">.</span><span class="n">FP16</span><span class="p">:</span>
            <span class="n">builder_config</span><span class="o">.</span><span class="n">set_flag</span><span class="p">(</span><span class="n">trt</span><span class="o">.</span><span class="n">BuilderFlag</span><span class="o">.</span><span class="n">FP16</span><span class="p">)</span>

        <span class="k">if</span> <span class="n">lower_precision</span> <span class="o">==</span> <span class="n">LowerPrecision</span><span class="o">.</span><span class="n">INT8</span><span class="p">:</span>
            <span class="n">builder_config</span><span class="o">.</span><span class="n">set_flag</span><span class="p">(</span><span class="n">trt</span><span class="o">.</span><span class="n">BuilderFlag</span><span class="o">.</span><span class="n">INT8</span><span class="p">)</span>

        <span class="k">if</span> <span class="n">sparse_weights</span><span class="p">:</span>
            <span class="n">builder_config</span><span class="o">.</span><span class="n">set_flag</span><span class="p">(</span><span class="n">trt</span><span class="o">.</span><span class="n">BuilderFlag</span><span class="o">.</span><span class="n">SPARSE_WEIGHTS</span><span class="p">)</span>

        <span class="k">if</span> <span class="n">strict_type_constraints</span><span class="p">:</span>
            <span class="n">builder_config</span><span class="o">.</span><span class="n">set_flag</span><span class="p">(</span><span class="n">trt</span><span class="o">.</span><span class="n">BuilderFlag</span><span class="o">.</span><span class="n">STRICT_TYPES</span><span class="p">)</span>

        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">optimization_profiles</span><span class="p">:</span>
            <span class="k">for</span> <span class="n">optimization_profile</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">optimization_profiles</span><span class="p">:</span>
                <span class="n">builder_config</span><span class="o">.</span><span class="n">add_optimization_profile</span><span class="p">(</span><span class="n">optimization_profile</span><span class="p">)</span>

        <span class="k">if</span> <span class="n">algorithm_selector</span><span class="p">:</span>
            <span class="n">builder_config</span><span class="o">.</span><span class="n">set_flag</span><span class="p">(</span><span class="n">trt</span><span class="o">.</span><span class="n">BuilderFlag</span><span class="o">.</span><span class="n">DISABLE_TIMING_CACHE</span><span class="p">)</span>
            <span class="n">builder_config</span><span class="o">.</span><span class="n">algorithm_selector</span> <span class="o">=</span> <span class="n">algorithm_selector</span>

        <span class="k">if</span> <span class="n">tactic_sources</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
            <span class="n">builder_config</span><span class="o">.</span><span class="n">set_tactic_sources</span><span class="p">(</span><span class="n">tactic_sources</span><span class="o">=</span><span class="n">tactic_sources</span><span class="p">)</span>

        <span class="n">engine</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">builder</span><span class="o">.</span><span class="n">build_engine</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">network</span><span class="p">,</span> <span class="n">builder_config</span><span class="p">)</span>
        <span class="k">assert</span> <span class="n">engine</span>

        <span class="n">serialized_cache</span> <span class="o">=</span> <span class="p">(</span>
            <span class="nb">bytearray</span><span class="p">(</span><span class="n">cache</span><span class="o">.</span><span class="n">serialize</span><span class="p">())</span>
            <span class="k">if</span> <span class="n">builder_config</span><span class="o">.</span><span class="n">get_timing_cache</span><span class="p">()</span>
            <span class="k">else</span> <span class="nb">bytearray</span><span class="p">()</span>
        <span class="p">)</span>
        <span class="n">_LOGGER</span><span class="o">.</span><span class="n">info</span><span class="p">(</span>
            <span class="sa">f</span><span class="s2">&quot;Build TRT engine elapsed time: </span><span class="si">{</span><span class="n">datetime</span><span class="o">.</span><span class="n">now</span><span class="p">()</span> <span class="o">-</span> <span class="n">build_engine_start_time</span><span class="si">}</span><span class="s2">&quot;</span>
        <span class="p">)</span>

        <span class="k">return</span> <span class="n">TRTInterpreterResult</span><span class="p">(</span>
            <span class="n">engine</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_input_names</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_output_names</span><span class="p">,</span> <span class="n">serialized_cache</span>
        <span class="p">)</span>

    <span class="k">def</span> <span class="nf">run_node</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n</span><span class="p">):</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_cur_node_name</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">n</span><span class="p">)</span>
        <span class="c1"># add &quot;_itensor_to_tensor_meta&quot;</span>
        <span class="n">kwargs</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">n</span><span class="o">.</span><span class="n">kwargs</span><span class="p">)</span>
        <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;_itensor_to_tensor_meta&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_itensor_to_tensor_meta</span>
        <span class="n">n</span><span class="o">.</span><span class="n">kwargs</span> <span class="o">=</span> <span class="n">kwargs</span>

        <span class="c1"># run the node</span>
        <span class="n">trt_node</span> <span class="o">=</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">run_node</span><span class="p">(</span><span class="n">n</span><span class="p">)</span>

        <span class="c1"># remove &quot;_itensor_to_tensor_meta&quot;</span>
        <span class="n">kwargs</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">n</span><span class="o">.</span><span class="n">kwargs</span><span class="p">)</span>
        <span class="k">del</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;_itensor_to_tensor_meta&quot;</span><span class="p">]</span>
        <span class="n">n</span><span class="o">.</span><span class="n">kwargs</span> <span class="o">=</span> <span class="n">kwargs</span>

        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">trt_node</span><span class="p">,</span> <span class="n">trt</span><span class="o">.</span><span class="n">tensorrt</span><span class="o">.</span><span class="n">ITensor</span><span class="p">):</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">_itensor_to_tensor_meta</span><span class="p">[</span><span class="n">trt_node</span><span class="p">]</span> <span class="o">=</span> <span class="n">n</span><span class="o">.</span><span class="n">meta</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;tensor_meta&quot;</span><span class="p">)</span>

        <span class="k">return</span> <span class="n">trt_node</span>

    <span class="k">def</span> <span class="nf">placeholder</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">):</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_input_names</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">target</span><span class="p">)</span>
        <span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">shape_ranges</span><span class="p">,</span> <span class="n">has_batch_dim</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">input_specs</span><span class="p">[</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">input_specs_iter</span>
        <span class="p">]</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">input_specs_iter</span> <span class="o">+=</span> <span class="mi">1</span>

        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">network</span><span class="o">.</span><span class="n">has_implicit_batch_dimension</span><span class="p">:</span>
            <span class="k">if</span> <span class="n">has_batch_dim</span><span class="p">:</span>
                <span class="n">shape</span> <span class="o">=</span> <span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">shape_range</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">shape_ranges</span><span class="p">):</span>
                <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">optimization_profiles</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">optimization_profiles</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">set_shape</span><span class="p">(</span><span class="n">target</span><span class="p">,</span> <span class="o">*</span><span class="n">shape_range</span><span class="p">)</span>

        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">network</span><span class="o">.</span><span class="n">add_input</span><span class="p">(</span>
            <span class="n">name</span><span class="o">=</span><span class="n">target</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="nb">tuple</span><span class="p">(</span><span class="n">shape</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch_dtype_to_trt</span><span class="p">(</span><span class="n">dtype</span><span class="p">)</span>
        <span class="p">)</span>

    <span class="k">def</span> <span class="nf">call_module</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">):</span>
        <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">target</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span>
        <span class="n">submod</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fetch_attr</span><span class="p">(</span><span class="n">target</span><span class="p">)</span>
        <span class="n">submod_type</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">submod</span><span class="p">,</span> <span class="s2">&quot;_base_class_origin&quot;</span><span class="p">,</span> <span class="nb">type</span><span class="p">(</span><span class="n">submod</span><span class="p">))</span>
        <span class="n">converter</span> <span class="o">=</span> <span class="n">CONVERTERS</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">submod_type</span><span class="p">)</span>

        <span class="k">if</span> <span class="ow">not</span> <span class="n">converter</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span>
                <span class="sa">f</span><span class="s2">&quot;Conversion of module of type </span><span class="si">{</span><span class="n">submod_type</span><span class="si">}</span><span class="s2"> not currently supported!&quot;</span>
            <span class="p">)</span>

        <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cur_node_name</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
        <span class="k">return</span> <span class="n">converter</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">network</span><span class="p">,</span> <span class="n">submod</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cur_node_name</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">call_function</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">):</span>
        <span class="n">converter</span> <span class="o">=</span> <span class="n">CONVERTERS</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">target</span><span class="p">)</span>

        <span class="k">if</span> <span class="ow">not</span> <span class="n">converter</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span>
                <span class="sa">f</span><span class="s2">&quot;Conversion of function </span><span class="si">{</span><span class="n">torch</span><span class="o">.</span><span class="n">typename</span><span class="p">(</span><span class="n">target</span><span class="p">)</span><span class="si">}</span><span class="s2"> not currently supported!&quot;</span>
            <span class="p">)</span>

        <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cur_node_name</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
        <span class="k">return</span> <span class="n">converter</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">network</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cur_node_name</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">call_method</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">):</span>
        <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">target</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span>
        <span class="n">converter</span> <span class="o">=</span> <span class="n">CONVERTERS</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">target</span><span class="p">)</span>

        <span class="k">if</span> <span class="ow">not</span> <span class="n">converter</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span>
                <span class="sa">f</span><span class="s2">&quot;Conversion of method </span><span class="si">{</span><span class="n">target</span><span class="si">}</span><span class="s2"> not currently supported!&quot;</span>
            <span class="p">)</span>

        <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cur_node_name</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
        <span class="k">return</span> <span class="n">converter</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">network</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cur_node_name</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">output</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">):</span>
        <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">args</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span>
        <span class="n">outputs</span> <span class="o">=</span> <span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="nb">tuple</span><span class="p">)</span> <span class="k">else</span> <span class="p">(</span><span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">],)</span>

        <span class="k">if</span> <span class="ow">not</span> <span class="nb">all</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">trt</span><span class="o">.</span><span class="n">tensorrt</span><span class="o">.</span><span class="n">ITensor</span><span class="p">)</span> <span class="k">for</span> <span class="n">output</span> <span class="ow">in</span> <span class="n">outputs</span><span class="p">):</span>
            <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;TensorRT requires all outputs to be Tensor!&quot;</span><span class="p">)</span>

        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">output</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">outputs</span><span class="p">):</span>
            <span class="k">if</span> <span class="nb">any</span><span class="p">(</span>
                <span class="n">op_name</span> <span class="ow">in</span> <span class="n">output</span><span class="o">.</span><span class="n">name</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;_&quot;</span><span class="p">)</span>
                <span class="k">for</span> <span class="n">op_name</span> <span class="ow">in</span> <span class="p">(</span>
                    <span class="s2">&quot;eq&quot;</span><span class="p">,</span>
                    <span class="s2">&quot;gt&quot;</span><span class="p">,</span>
                    <span class="s2">&quot;lt&quot;</span><span class="p">,</span>
                    <span class="s2">&quot;or&quot;</span><span class="p">,</span>
                    <span class="s2">&quot;xor&quot;</span><span class="p">,</span>
                    <span class="s2">&quot;and&quot;</span><span class="p">,</span>
                    <span class="s2">&quot;not&quot;</span><span class="p">,</span>
                    <span class="s2">&quot;ne&quot;</span><span class="p">,</span>
                    <span class="s2">&quot;isinf&quot;</span><span class="p">,</span>
                    <span class="s2">&quot;any&quot;</span><span class="p">,</span>
                <span class="p">)</span>
            <span class="p">):</span>
                <span class="n">output_bool</span> <span class="o">=</span> <span class="kc">True</span>
            <span class="k">else</span><span class="p">:</span>
                <span class="n">output_bool</span> <span class="o">=</span> <span class="kc">False</span>
            <span class="n">name</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;output</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">&quot;</span>
            <span class="n">output</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="n">name</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">network</span><span class="o">.</span><span class="n">mark_output</span><span class="p">(</span><span class="n">output</span><span class="p">)</span>
            <span class="k">if</span> <span class="n">output_bool</span><span class="p">:</span>
                <span class="n">output</span><span class="o">.</span><span class="n">dtype</span> <span class="o">=</span> <span class="n">trt</span><span class="o">.</span><span class="n">bool</span>
            <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_fp16</span> <span class="ow">and</span> <span class="n">output</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="n">trt</span><span class="o">.</span><span class="n">float32</span><span class="p">:</span>
                <span class="n">output</span><span class="o">.</span><span class="n">dtype</span> <span class="o">=</span> <span class="n">trt</span><span class="o">.</span><span class="n">float16</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">_output_names</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">name</span><span class="p">)</span></div>
</pre></div>

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